Genetic interactions effects for cancer disease identification using computational models: a review

Manavalan, R.; Priya, S.

doi:10.1007/s11517-021-02343-9

Cited by 6 publications

(4 citation statements)

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“…The MDR method was the first innovative tool generated mostly to detect and categorize non-additive genetic interactions in population-based investigations of human disease. The initial version of the MDR approach was developed in 2001 by Ritchie et al to identify SNPs interactions correlated with data related to breast cancer [46,64].…”

Section: Discussionmentioning

confidence: 99%

“…The MDR method has been successfully applied to the detection of epistasis or gene-gene interactions in a variety of complex human diseases, including some cancers with different locations. In a literature review [64], several works on the application of MDR for the analysis of gene-gene interactions are cited. In order to identify genetic variations linked to different diseases and environmental factors like smoking and air pollution, Manuguerra et al [75] used MDR on patients with myeloid leukaemia, bladder cancer, and lung cancer.…”

Section: Discussionmentioning

confidence: 99%

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SNPs-Panel Polymorphism Variations in GHRL and GHSR Genes Are Not Associated with Prostate Cancer

Merabet,

Ramoz,

Boulmaiz

et al. 2023

Biomedicines

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Prostate cancer (PCa) is a major public health problem worldwide. Recent studies have suggested that ghrelin and its receptor could be involved in the susceptibility to several cancers such as PCa, leading to their use as an important predictive way for the clinical progression and prognosis of cancer. However, conflicting results of single nucleotide polymorphisms (SNPs) with ghrelin (GHRL) and its receptor (GHSR) genes were demonstrated in different studies. Thus, the present case–control study was undertaken to investigate the association of GHRL and GHSR polymorphisms with the susceptibility to sporadic PCa. A cohort of 120 PCa patients and 95 healthy subjects were enrolled in this study. Genotyping of six SNPs was performed: three tag SNPs in GHRL (rs696217, rs4684677, rs3491141) and three tag SNPs in the GHSR (rs2922126, rs572169, rs2948694) using TaqMan. The allele and genotype distribution, as well as haplotypes frequencies and linked disequilibrium (LD), were established. Multifactor dimensionality reduction (MDR) analysis was used to study gene–gene interactions between the six SNPs. Our results showed no significant association of the target polymorphisms with PCa (p > 0.05). Nevertheless, SNPs are often just markers that help identify or delimit specific genomic regions that may harbour functional variants rather than the variants causing the disease. Furthermore, we found that one GHSR rs2922126, namely the TT genotype, was significantly more frequent in PCa patients than in controls (p = 0.040). These data suggest that this genotype could be a PCa susceptibility genotype. MDR analyses revealed that the rs2922126 and rs572169 combination was the best model, with 81.08% accuracy (p = 0.0001) for predicting susceptibility to PCa. The results also showed a precision of 98.1% (p < 0.0001) and a PR-AUC of 1.00. Our findings provide new insights into the influence of GHRL and GHSR polymorphisms and significant evidence for gene–gene interactions in PCa susceptibility, and they may guide clinical decision-making to prevent overtreatment and enhance patients’ quality of life.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

SNPs-Panel Polymorphism Variations in GHRL and GHSR Genes Are Not Associated with Prostate Cancer

Merabet,

Ramoz,

Boulmaiz

et al. 2023

Biomedicines

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“…Mathematical models in oncology can help to understand the mechanisms of oncogenesis, cancer progression, and optimal treatment [2]. Computational models have revealed extensive pairwise epistasis among germline variants and among gene knockdowns [3, 4], and approaches have been applied to identify sets of variants that are sufficient to cause cancer [5]. Among somatic mutations, patterns of mutual exclusivity have been interpreted as a consequence of antagonistic epistasis, and patterns of co-mutation have been interpreted as a consequence of synergistic epistasis.…”

Section: Introductionmentioning

confidence: 99%

“…The estimation of the order of epistatic effects in cancer is particularly challenging because most large tumor sequence datasets provide only one time point of tumor evolution at the time of tumor biopsy or excision [12]. Most evaluate correlations between the frequencies of mutations [3], yet correlations can arise either because of selective epistasis or because of commonalities of mutation process between selected sites. There are several orders of magnitude of difference in the rates at which somatic mutations occur in different genes and sites within the genome.…”

Section: Introductionmentioning

confidence: 99%

Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis

Alfaro-Murillo

Townsend

2022

Preprint

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Cancer occurs as a consequence of multiple somatic mutations that lead to uncontrolled cell growth. Mutual exclusivity and co-occurrence of mutations imply---but do not prove---that they can exert synergistic or antagonistic epistatic effects on oncogenesis. Knowledge of these interactions, and the consequent trajectories of mutation and selection that lead to cancer has been a longstanding goal within the cancer research community. Recent research has revealed mutation rates and scaled selection coefficients for specific recurrent variants across many cancer types. However, estimation of pairwise and higher-order effects---essential to estimation of the trajectory of likely cancer genotoypes---has been a challenge. Therefore, we have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation (flux), dependent on somatic cancer genotype. Coupling the continuous-time Markov chain model with a deconvolution approach provides estimates of underlying rates of mutation and selection across the trajectory of oncogenesis. We demonstrate computation of fluxes and selection coefficients in a somatic evolutionary model for the four most frequently variant driver genes (TP53, LRP1B, KRAS and STK11) from 585 cases of lung adenocarcinoma. Our analysis reveals multiple antagonistic epistatic effects that reduce the possible routes of oncogenesis, and inform cancer research regarding viable trajectories of somatic evolution whose progression could be forestalled by precision medicine. Synergistic epistatic effects are also identified, most notably in the somatic genotype TP53+LRP1B for mutations in the KRAS gene, and in somatic genotypes containing KRAS or TP53 mutations for mutations in the STK11 gene. Large positive fluxes of KRAS variants were driven by large selection coefficients, whereas the flux toward LRP1B mutations was substantially aided by a large mutation rate for this gene. The approach enables inference of the most likely routes of site-specific variant evolution and estimation of the strength of selection operating on each step along the route, a key component of what we need to know to develop and implement personalized cancer therapies.

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Reinforcement control with fuzzy-rules emulated network for robust-optimal drug-dosing of cancer dynamics

Treesatayapun

Muñoz‐Vázquez

2023

Neural Comput & Applic

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Genetic interactions effects for cancer disease identification using computational models: a review

Cited by 6 publications

References 100 publications

SNPs-Panel Polymorphism Variations in GHRL and GHSR Genes Are Not Associated with Prostate Cancer

SNPs-Panel Polymorphism Variations in GHRL and GHSR Genes Are Not Associated with Prostate Cancer

Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis

Reinforcement control with fuzzy-rules emulated network for robust-optimal drug-dosing of cancer dynamics

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